Knowledge discovery from mining the association between H5N1 outbreaks and environmental factors

The global spread of highly pathogenic avian influenza H5N1 in poultry, wild birds and humans, poses a significant panzootic threat and a serious public health risk. An efficient surveillance and disease control system requires a deep understanding of their spread mechanisms, including environmental factors responsible for the outbreak of the disease. Previous studies suggested that H5N1 viruses occurred under specific environmental circumstances in Asia and Africa. These studies were mainly derived from poultry outbreaks. In Europe, a large number of wild bird outbreaks were reported in west Europe with few or no poultry infections nearby. This distinct outbreak pattern in relation to environmental characteristics, however, has not yet been explored. This research demonstrated the use of logistic regression analyses to examine quantitative associations between anthropogenic and physical environmental factors, and the wild bird H5N1outbreaks in Europe. A geographic information system is used to visualize and analyze the data. Our results indicate that the H5N1 outbreaks occur in wild birds in Europe under predictable environmental conditions, which are highly correlated with increased NDVI in December, decreased aspect and slope, increased minimum temperature in October and decreased precipitation in January. It suggests that H5N1 outbreaks in wild birds are strongly influenced by food resource availability and facilitated by the increased temperature and the decreased precipitation. We therefore deduce that the H5N1 outbreaks in wild birds in Europe may be mainly caused by contact with wild birds. These findings are of great importance for global surveillance of H5N1 outbreaks in wild birds

A normalized difference vegetation index (NDVI) time-series of idle agriculture lands: A preliminary study

In this paper, the NDVI time-series collected from the study area between year 2003 and 2005 of all land cover types are plotted and compared. The study area is the agricultural zones in Banphai District, Khonkean, Thailand. The LANDSAT satellite images of different dates were first transformed into a time series of Normalized Difference Vegetation Index (NDVI) images before the investigation. It can be visually observed that the NDVI time series of the Idle Agriculture Land (IAL) has the NDVI values closed to zero. In other words, the trend of the NDVI values remains, approximately, unchanged about the zero level for the whole period of the study time. In contrast, the non-idle areas hold a higher level of the NDVI variation. The NDVI values above 0.5 can be found in these non-idle areas during the growing seasons. Thus, it can be hypothesized that the NDVI time-series of the different land cover types can be used for IAL classification. This outcome is a prerequisite to the follow-up study of the NDVI pattern classification that will be done in the near future

Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Environmental factors influencing the spread of the highly pathogenic avian influenza H5N1 virus in wild birds in Europe

A large number of occurrences of the highly pathogenic avian influenza (HPAI) H5N1 virus in wild birds were reported in Europe. The relationship between the occurrence pattern and environmental factors has, however, not yet been explored. This research uses logistic regression to quantify the relationships between anthropogenic or physical environmental factors and HPAI H5N1 occurrences. Our results indicate that HPAI H5N1 occurrences are highly correlated with the following: the increased normalized difference vegetation index (NDVI) in December; intermediate NDVI in March; lower elevations; increased minimum temperatures in January; and reduced precipitation in January. A predictive risk map of HPAI H5N1 occurrences in wild birds in Europe was generated on the basis of five key environmental factors. Independent validation of the risk map showed the predictive model to be of high accuracy (79%). The analysis suggests that HPAI H5N1 occurrences in wild birds are strongly influenced by the availability of food resources and are facilitated by increased temperatures and reduced precipitation. We therefore deduced that HPAI H5N1 occurrences in wild birds in Europe are probably caused by contact with other wild birds and not by contact with domestic poultry. These findings are important considerations for the global surveillance of HPAI H5N1 occurrences in wild birds